{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "#### bar 条形图: 一组分类, 一组数值\n",
    "- seaborn.catplot(kind='bar'): figure-level\n",
    "- seaborn.barplot(): axes-level"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "outputs": [],
   "source": [
    "df1 = pd.read_csv('./input/athlete_events.csv')\n",
    "df2 = pd.read_csv('./input/noc_regions.csv')\n",
    "df = pd.merge(\n",
    "    df1, df2,\n",
    "    on='NOC',\n",
    "    how='left',\n",
    "    validate='m:1')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "outputs": [],
   "source": [
    "sns.set(\n",
    "    context='talk',  # paper, notebook, talk, poster\n",
    "    style='darkgrid',  # darkgrid, whitegrid, dark, white, ticks\n",
    "    palette='pastel',  # deep,muted,bright,pastel,dark,colorblind,hls,husl,cm\n",
    "    rc={\n",
    "        'figure.figsize': [8, 6]\n",
    "    }\n",
    ")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 187409 entries, 0 to 271115\n",
      "Data columns (total 17 columns):\n",
      " #   Column  Non-Null Count   Dtype  \n",
      "---  ------  --------------   -----  \n",
      " 0   ID      187409 non-null  int64  \n",
      " 1   Name    187409 non-null  object \n",
      " 2   Sex     187409 non-null  object \n",
      " 3   Age     180685 non-null  float64\n",
      " 4   Height  148551 non-null  float64\n",
      " 5   Weight  147204 non-null  float64\n",
      " 6   Team    187409 non-null  object \n",
      " 7   NOC     187409 non-null  object \n",
      " 8   Games   187409 non-null  object \n",
      " 9   Year    187409 non-null  int64  \n",
      " 10  Season  187409 non-null  object \n",
      " 11  City    187409 non-null  object \n",
      " 12  Sport   187409 non-null  object \n",
      " 13  Event   187409 non-null  object \n",
      " 14  Medal   28531 non-null   object \n",
      " 15  region  187171 non-null  object \n",
      " 16  notes   3344 non-null    object \n",
      "dtypes: float64(3), int64(2), object(12)\n",
      "memory usage: 25.7+ MB\n"
     ]
    }
   ],
   "source": [
    "df3 = df.drop_duplicates(\n",
    "    # 重复数据只保留一行就可以了\n",
    "    subset=['ID', 'Year']\n",
    ")\n",
    "df3.info()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "outputs": [
    {
     "data": {
      "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x7fdad153f9d0>"
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 576x432 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(\n",
    "    x='Season',\n",
    "    y='Height',\n",
    "    data=df3\n",
    ")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "outputs": [
    {
     "data": {
      "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x7fdaccb86c50>"
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 576x432 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(\n",
    "    x='Height',\n",
    "    y='Season', # 分类类型\n",
    "    data=df3\n",
    ")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "outputs": [
    {
     "data": {
      "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x7fdacb695d10>"
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 576x432 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(\n",
    "    x='Season',\n",
    "    y='Height',\n",
    "    hue='Sex',\n",
    "    data=df3,\n",
    "    ci=False,\n",
    "    order=['Winter', 'Summer'],\n",
    "    hue_order=['F', 'M']\n",
    ")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "name": "python3",
   "language": "python",
   "display_name": "Python 3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "3.7.6-final"
  },
  "pycharm": {
   "stem_cell": {
    "cell_type": "raw",
    "source": [],
    "metadata": {
     "collapsed": false
    }
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}